Stochastic Methods

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization AISTATS 2016 Chained Gaussian Processes AISTATS 2016 CMA-ES with Optimal Covariance Update and Storage Complexity NeurIPS 2016 Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms JMLR 2016 Consistently Estimating Markov Chains with Noisy Aggregate Data AISTATS 2016 Control Functionals for Quasi-Monte Carlo Integration AISTATS 2016 Cyclades: Conflict-free Asynchronous Machine Learning NeurIPS 2016 Determinantal Regularization for Ensemble Variable Selection AISTATS 2016 Differentially Private Data Releasing for Smooth Queries JMLR 2016 Distributed Coordinate Descent Method for Learning with Big Data JMLR 2016 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016 Dual Space Gradient Descent for Online Learning NeurIPS 2016 Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis NeurIPS 2016 Efficient Nonparametric Smoothness Estimation NeurIPS 2016 Entropy Coding of Spectral Envelopes for Speech and Audio Coding Using Distribution Quantization Interspeech 2016 Epigraph projections for fast general convex programming ICML 2016 Estimating Maximum Expected Value through Gaussian Approximation ICML 2016 Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums JMLR 2016 Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NeurIPS 2016 Fast and Provably Good Seedings for k-Means NeurIPS 2016 Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016 Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier ICML 2016 Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NeurIPS 2016 Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity ICML 2016